Multiobjective Lagrangian duality for portfolio optimization with risk measures
AbstractIn this paper we present an application for a multiobjective optimization problem. The objective functions of the primal problem are the risk and the expected pain associated to a portfolio vector. Then, we present a Lagrangian dual problem for it. In order to formulate this problem, we introduce the theory about risk measures for a vector of random variables. The definition of this kind of measures is a very evolving topic; moreover, we want to measure the risk in the multidimensional case without exploiting any scalarization technique of the random vector. We refer to the approach of the image space analysis in order to recall weak and strong Lagrangian duality results obtained through separation arguments. Finally, we interpret the shadow prices of the dual problem providing new definitions for risk aversion and non-satiability in the linear case.
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Bibliographic InfoPaper provided by University of Verona, Department of Economics in its series Working Papers with number 18/2010.
Date of creation: Dec 2010
Date of revision:
Multivariate risk measures; Vector Optimization; Lagrangian Duality; Shadow prices; Image Space Analysis.;
Find related papers by JEL classification:
- C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
- D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
- G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
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